Evaluating and Mitigating Errors in LLM-Generated Web API Integrations
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911324774072320 |
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| author | Maninger, Daniel Chemnitz, Leon Sharifloo, Amir Molzam Lamba, Tushar Brugger, Jannis Mezini, Mira |
| author_facet | Maninger, Daniel Chemnitz, Leon Sharifloo, Amir Molzam Lamba, Tushar Brugger, Jannis Mezini, Mira |
| contents | API integration is a cornerstone of our digital infrastructure, enabling software systems to connect and interact. However, as shown by many studies, writing or generating correct code to invoke APIs, particularly web APIs, is challenging. Although large language models (LLMs) have become popular in software development, their effectiveness in automating the generation of web API integration code remains unexplored. In order to address this, we present WAPIIBench, a dataset and evaluation pipeline designed to assess the ability of LLMs to generate web API invocation code. Our experiments with several open-source LLMs reveal that generating API invocations poses a significant challenge, resulting in hallucinated endpoints, incorrect argument usage, and other errors. None of the evaluated open-source models was able to solve more than 40% of the tasks. Motivated by those findings, we explore the potential of constrained decoding for generating API invocations. To this end, we propose an automatic translation from API specifications to constraints. Our approach prevents violations of API usage rules and significantly increases the overall correctness of the generated code, on average by 90% and 135%, depending on the provided starter code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating and Mitigating Errors in LLM-Generated Web API Integrations Maninger, Daniel Chemnitz, Leon Sharifloo, Amir Molzam Lamba, Tushar Brugger, Jannis Mezini, Mira Software Engineering Machine Learning API integration is a cornerstone of our digital infrastructure, enabling software systems to connect and interact. However, as shown by many studies, writing or generating correct code to invoke APIs, particularly web APIs, is challenging. Although large language models (LLMs) have become popular in software development, their effectiveness in automating the generation of web API integration code remains unexplored. In order to address this, we present WAPIIBench, a dataset and evaluation pipeline designed to assess the ability of LLMs to generate web API invocation code. Our experiments with several open-source LLMs reveal that generating API invocations poses a significant challenge, resulting in hallucinated endpoints, incorrect argument usage, and other errors. None of the evaluated open-source models was able to solve more than 40% of the tasks. Motivated by those findings, we explore the potential of constrained decoding for generating API invocations. To this end, we propose an automatic translation from API specifications to constraints. Our approach prevents violations of API usage rules and significantly increases the overall correctness of the generated code, on average by 90% and 135%, depending on the provided starter code. |
| title | Evaluating and Mitigating Errors in LLM-Generated Web API Integrations |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2509.20172 |